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Reassessing Data Quality for Information Products

Author

Listed:
  • Debabrata Dey

    (Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195)

  • Subodha Kumar

    (Mays Business School, Texas A& M University, College Station, Texas 77843)

Abstract

The quality profile of information retrieved from a database using a query is quite important in the context of managerial decision making. Parssian et al. (Parssian, A., S. Sarkar, V. S. Jacob. 2004. Assessing data quality for information products: Impact of selection, projection, and Cartesian product. Management Sci. 50(7) 967-982) propose a methodology to determine the quality profile of the result of a query from the quality profile of the source data. Although they consider an important problem, and the proposed methodology is quite useful in practice, some of their results for the selection operation are not correct in general. Here, we identify these errors and present appropriate corrections.

Suggested Citation

  • Debabrata Dey & Subodha Kumar, 2010. "Reassessing Data Quality for Information Products," Management Science, INFORMS, vol. 56(12), pages 2316-2322, December.
    • Handle: RePEc:inm:ormnsc:v:56:y:2010:i:12:p:2316-2322
    DOI: 10.1287/mnsc.1100.1261
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    References listed on IDEAS

    as
    1. Kon, Henry B. & Madnick, Stuart E. & Siegel, Michael D., 1995. "Good answers from bad data : a data management strategy," Working papers 3868-95., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    2. Amir Parssian & Sumit Sarkar & Varghese S. Jacob, 2004. "Assessing Data Quality for Information Products: Impact of Selection, Projection, and Cartesian Product," Management Science, INFORMS, vol. 50(7), pages 967-982, July.
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    Citations

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    Cited by:

    1. Tiwari, Sunil & Sharma, Pankaj & Choi, Tsan-Ming & Lim, Andrew, 2023. "Blockchain and third-party logistics for global supply chain operations: Stakeholders’ perspectives and decision roadmap," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    2. Kunwar Raghvendra Singh & Ankit Pratim Goswami & Ajay S. Kalamdhad & Bimlesh Kumar, 2020. "Development of irrigation water quality index incorporating information entropy," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(4), pages 3119-3132, April.
    3. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    4. Satyavati Shukla & Shirishkumar Gedam & M. V. Khire, 2020. "Implications of demographic changes and land transformations on surface water quality of rural and urban subbasins of Upper Bhima River basin, Maharashtra, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(1), pages 129-171, January.
    5. Qi Liu & Gengzhong Feng & Giri Kumar Tayi & Jun Tian, 2021. "Managing Data Quality of the Data Warehouse: A Chance-Constrained Programming Approach," Information Systems Frontiers, Springer, vol. 23(2), pages 375-389, April.
    6. Xiangyu Chang & Yinghui Huang & Mei Li & Xin Bo & Subodha Kumar, 2021. "Efficient Detection of Environmental Violators: A Big Data Approach," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1246-1270, May.
    7. Debabrata Dey & Subodha Kumar, 2013. "Data Quality of Query Results with Generalized Selection Conditions," Operations Research, INFORMS, vol. 61(1), pages 17-31, February.

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